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Semantic based file retrieval on resource limited devices with ontology alignment support

Semantic based file retrieval on resource limited devices wi...

引用

2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Jan, Sadaqat Li, Maozhen Al-Sultany, Ghaidaa Al-Raweshidy, Hamed School of Engineering and Design Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University China

ISBN: (纸本)9781612841816

In this paper, we present a file search mechanism that can utilize the ontology alignments to facilitate file retrieval on resource-limited devices by exploiting the knowledge of more than one ontologies. This enables the device users to take advantage of all ontologies, which might have been developed in the same domain. A semantic-based file retrieval framework was previously presented for resource-limited devices, which annotates files with its basic attributes and provides file retrieval capabilities. A generic ontology was developed to define the most commonly used keywords which can be used as metadata of the stored files. The ontology was employed to exploit the semantic based file retrieval capability. The search mechanism was evaluated by formulating two test cases. In first test case the framework make use of single ontology while in the second test case, the ontology alignments were utilized by aligning two ontologies. The implications of ontology alignment support were analyzed through probabilistic evaluation, which shows the progress in file retrieval efficiency. © 2011 IEEE.

关键词： Ontology

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Ontology alignment using Rough Sets

Ontology alignment using Rough Sets

引用

2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

ISBN: (纸本)9781612841816

Ontology alignment has been one of the most significant research issues to bridge the semantic gap between heterogeneous systems toward Semantic Web. Many proposed systems in recent years, use multiple similarity measures and variety of aggregating algorithms to map entities from two different ontologies. However, the real issue arises when alignment systems turn out to be uncertain about entities which are neither completely similar nor dissimilar. This paper presents an ontology alignment system which uses a combination of various similarity measures in order to map entities from two different ontologies. Rough Sets have been used to deal with uncertainties during the mapping process by defining the similarity measures as the attributes of entities. The proposed alignment system employs the combination of string based, linguistic based and structural matchers. The structural matcher compares the super-classes, sub-classes and properties of two class entities. We use WordNet to compare the linguistic similarity between entities in the mapping process. The performance of proposed system is evaluated in terms of precision and recall and compared with existing state of the art alignment systems. For this purpose, the Ontology Alignment Evaluation Initiative (OAEI) benchmark group-3 test ontologies have been used. Experimental tests show that the performance our proposed alignment system is comparable with existing alignment systems. © 2011 IEEE.

关键词： Ontology

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Performance evaluation of Latent Dirichlet Allocation in text mining

Performance evaluation of Latent Dirichlet Allocation in tex...

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2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Liu, Zelong Li, Maozhen Liu, Yang Ponraj, Mahesh School of Engineering and Design Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University China

ISBN: (纸本)9781612841816

This paper introduces three classic models of statistical topic models: Latent Semantic Indexing (LSI), Probabilistic Latent Semantic Indexing (PLSI) and Latent Dirichlet Allocation (LDA). Then a method of text classification based on LDA model is briefly described, which uses LDA model as a text representation method. Each document means a probability distribution of fixed latent topic sets. Next, Support Vector Machine (SVM) is chose as classification algorithm. Finally, the evaluation parameters in classification system of LDA with SVM are higher than other two methods which are LSI with SVM and VSM with SVM, showing a better classification performance. © 2011 IEEE.

关键词： Support vector machines

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Parallelizing multiclass Support Vector Machines for scalable image annotation

Parallelizing multiclass Support Vector Machines for scalabl...

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2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Alham, Nasullah Khalid Li, Maozhen Liu, Yang Hammoud, Suhel School of Engineering and Design Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University China

ISBN: (纸本)9781612841816

Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) are used extensively due to their generalization properties. SVM was initially designed for binary classifications. However, most classification problems arising in domains such as image annotation usually involve more than two classes. Notably SVM training is a computationally intensive process especially when the training dataset is large. This paper presents MRMSVM, a distributed multiclass SVM algorithm for large scale image annotation which partitions the training dataset into smaller binary chunks and train SVM in parallel using a cluster of computers. MRMSVM is evaluated in an experimental environment showing that the distributed Multiclass SVM algorithm reduces the training time significantly while maintaining a high level of accuracy in classifications. © 2011 IEEE.

关键词： MapReduce

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Ontology based file retrieval framework for resource limited mobile devices

Ontology based file retrieval framework for resource limited...

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2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

ISBN: (纸本)9781612841816

Recent advances in mobile technologies have emerged new challenges for researchers. File management and retrieval on resource-limited devices has become a challenging problem as these devices are now equipped with larger storage capacities. This paper presents an overview about the possibilities of implementing Semantic Web Technologies in order to facilitate the file retrieval on resource-limited devices. We have proposed a semantic based file retrieval framework which utilizes a generic ontology in file search process. The search module is implemented in Java ME platforms as a case study. Performance of the proposed search module is evaluated through experimental tests along with probabilistic evaluations. The implemented module not only automates file retrieval process but also shows significant improvement in file retrieval efficiency by utilizing the knowledge of employed ontology in the search module. © 2011 IEEE.

关键词： Ontology

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design electronic botany worksheet generation based on bloom's taxonomy for mobile learning

Design electronic botany worksheet generation based on bloom...

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International Conference on Advanced Learning Technologies

作者： Kuo, Rita Ying, Keith Chang, Maiga Heh, Jia-Sheng Department of Digital Design Mingdao University ChangHua Taiwan Dept. of Information and Computer Engineering Chung-Yuan Christian University Chung-Li Taiwan School of Computing and Information Systems Athabasca University Edmonton Canada

ISBN: (纸本)9780769543468

Worksheet is a common tool for teachers guiding students learning a subject. This research designs an automatic worksheet generator for constructing a worksheet according to the results of student's online exam. We focus on the worksheet of botany observation and analyze 47 different activities in the worksheet. The relation between the worksheet activity and its cognitive ability can be used to decide what activity should be constructed based on students' test result. A simple example is demonstrated in the end of this paper. © 2011 IEEE.

关键词： Students

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Neuro-PID adaptive control scheme for blood pressure regulation

Neuro-PID adaptive control scheme for blood pressure regulat...

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International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA)

作者： S Enbiya A. Hossain F. Mahieddine School of Computing Informatics and Media University of Bradford Bradford UK School of Computing Engineering and Information Sciences Northumbria University UK School of Engineering Design and Technology University of Bradford Bradford UK

Control of physiological states such as mean arterial pressure (MAP) has been successfully achieved using single drug by different control algorithms. Multi-drug delivery demonstrates a significantly challenging task as compared to control with a single-drug. Also the patient's sensitivity to the drugs varies from patient to patient. Therefore, the implementation of adaptive controller is very essential to improve the patient care in order to reduce the workload of healthcare staff and costs. This paper presents the design and implementation of a Proportional Integral Derivative controller (PID) using neural network based parameter tuning mechanism to regulate mean arterial pressure and cardiac output (CO) by administering vasoactive and inotropic drugs that are sodium nitroprusside (SNP) and dopamine (DPM) respectively. The parameters of PID controller were optimised offline using Simulink Response Optimization tool. The proposed Neuro-PID controller has been implemented, tested and verified to demonstrate its merits and capabilities as compared to the existing approaches to cover wide range of patients.

关键词： Drugs Blood pressure Adaptation models Mathematical model Sensitivity Control systems Adaptive control

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Analysis of Supervised Text Classification Algorithms on Corporate Sustainability Reports

Analysis of Supervised Text Classification Algorithms on Cor...

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2011 International Conference on Computer Science and Network Technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

作者： Amir Mohammad Shahi Biju Issac Jashua Rajesh Modapothala School of Engineering Computing and Science School of Business and Design Swinburne University of Technology (Sarawak Campus)Kuching Malaysia

Machine Learning approach to text classification has been the dominant method in the research and application field since it was first introduced in the 1990s. It has been proven that document classification applications based on Machine Learning produce competitive results to those based on the Knowledge Based approaches. This approach has been widely researched upon as well as applied in various applications to solve various text categorization problems. In this research we have applied such techniques in a novel effort to find out which document classification algorithms perform best on Corporate Sustainability Reports.

关键词： Machine Learning Feature Selection Text Classification Document Categorization Supervised Learning Corporate Sustainability Report GRI

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Customer profile classification using transactional data

Customer profile classification using transactional data

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World Congress on Nature & Biologically Inspired computing (NaBIC)

作者： Edward T. Apeh Bogdan Gabrys Amanda Schierz Smart Technology Research Centre School of Design Engineering and Computing Bournemouth University Dorset UK

Customer profiles are by definition made up of factual and transactional data. It is often the case that due to reasons such as high cost of data acquisition and/or protection, only the transactional data are available for data mining operations. Transactional data, however, tend to be highly sparse and skewed due to a large proportion of customers engaging in very few transactions. This can result in a bias in the prediction accuracy of classifiers built using them towards the larger proportion of customers with fewer transactions. This paper investigates an approach for accurately and confidently grouping and classifying customers in bins on the basis of the number of their transactions. The experiments we conducted on a highly sparse and skewed real-world transactional data show that our proposed approach can be used to identify a critical point at which customer profiles can be more confidently distinguished.

关键词： Accuracy Data mining Data models Business Prediction algorithms Biology Support vector machines

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Multi-level power and performance tradeoffs across the access networks to customer premises equipment

Multi-level power and performance tradeoffs across the acces...

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International Green computing Conference

作者： Jain, V. Parr, G.P. Morrow, P.J. Polaine, M. India-UK Centre of Excellence NGNs School of Computing and Information Engineering University of Ulster Coleraine BT52 1SA United Kingdom BT Innovate and Design Adastral Park Martlesham United Kingdom

ISBN: (纸本)9781457712203

Across broadband access networks, significant opportunities to minimise energy consumption exist at the application and networks level but are not fully coordinated. For example, an application can tune its QoS in accordance with the available power levels of network systems. As a first step towards enabling energy efficiency, we study the inter-relationship between energy consumption and application performance in a residential home-gateway. The study reveals the power and application performance trade-offs in broadband access networks and then employs an agent based approach that works together in accordance with a flexible set of power-performance objectives and determines the existence of a feasible coordinated operating point. The key elements of our approach include (a) a power-performance polygon-based methodology which automatically selects power management settings that keep the pathway from access network to home-gateway operating close to the optimal operating point (b) a multi-agent policy-based approach which adaptively manages performance and power consumption based on varying traffic load during peak/off-peak hours. Finally, the efficiency of the model is demonstrated by the initial results of the experiments. © 2011 IEEE.

关键词： Energy utilization

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